The Loop the Seat Closes
What the driver’s seat reveals about consequence, feedback, and the variable both post-labor and AI optimism miss.
The Loop, From the Seat
Last time, in “The Concession That Widened My World,” I wrote about why driving Uber filled a hole that a year of research could not. That was the personal account. This is the structural one.
Strip the sentiment away and look at what the driver’s seat actually is. It is a feedback loop, and a remarkably tight one. I pay attention to the road and to the person in the back. I act. The consequence arrives immediately, and it is mine: the turn was right or wrong, the rider relaxed or tensed, the route saved time or cost it. I see the result, I carry it, and my judgment updates before the next ride. Attention, action, consequence, feedback, revised judgment, all inside a few minutes.
I call that circuit the attention-experience loop, and I think it is the thing intelligence is actually built from: not raw processing, but attention that meets consequence and gets corrected. What struck me behind the wheel is how completely the loop runs there, and how rarely the debates about the future of work mention it at all. We argue about income, and we argue about output. The loop is the variable nobody is pricing.
What UBI Pays You to Leave
Start with the post-labor proposal, because it is the cleaner error. When automation takes the jobs, the dominant answer is a universal basic income. Replace the lost wages, and the problem is handled.
But the loop was never about wages. A monthly deposit replaces the income a job produces. It does not replace the circuit the job ran. It pays you, quite precisely, to stand outside the loop: to have attention with no required action, action with no consequence that lands on you, output with no feedback that bites.
I know this because I lived the funded version of it without the funding. For a year, I had purpose, a body of work, an identity I believed in. What I did not have was a closed loop. My attention went out, and very little came back. A UBI would not have fixed that. It would have underwritten it indefinitely and made the underwriting feel like a solution.
The Slack Loop
The post-labor case is easy to see because the loop is cut cleanly. The harder case, and the one that should worry us more, is the loop that stays connected but goes slack.
This is the subtler thing that happens as we route work through AI, and it shows up most clearly not in the expert but in the apprentice. A few weeks ago, I had a junior lawyer in the back seat. She told me AI was only modestly useful to her, and she was clear about why: she does not yet have the experience to know when it is wrong. A partner does. Put the same tool in a partner’s hands, and it becomes powerful because the partner can weigh the output against years of being right, being wrong, and paying for the difference.
Sit with that, because it is the whole problem in one ride. The partner’s judgment was built inside a closed loop: act, bear the consequence, correct, repeat, for years. The tool can imitate the partner’s output. It cannot give the junior the loop that produced it. And if the next generation of associates leans on the tool through the very years that judgment is supposed to form, the loop never closes. We are not deskilling the experts so much as never-skilling the novices, handing them a way around the crucible that makes one. The loss compounds across generations because each cohort skips the formative process in which the last one was forged.
That is the slack loop. The wire is still connected. You still act, you ship the memo, you approve the recommendation. But AI makes it easy to bypass the judgment the action used to require, and when judgment goes, attention follows: you stop looking closely at what nothing depends on you getting right. The consequence, when it comes, is deferred or diffuse or absorbed by the system before it reaches you. The loop is drawn on the diagram. The current barely flows. And when feedback does arrive, it often closes around the wrong thing. The junior lawyer learns whether the model’s draft was good enough to pass, not whether her own judgment was sound. The loop closes, but on prompting and checking a machine, not on thinking like a lawyer.
What accumulates in that gap I call cognitive debt: judgment made without bearing its consequences, building up quietly until the day the judgment you outsourced is the one you need and find you never built. The point is not that fast loops are good and slow ones bad. Science, parenting, and good strategy run on loops that take years, and they matter enormously. The point is whether the consequence ever returns to the person at all. A slow loop still teaches. A severed one does not.
Here is the part that should be uncomfortable. The driver’s seat, one of the least prestigious jobs on offer, runs a near-perfect loop. A great deal of elevated, well-paid knowledge work, routed through tools that promise to do the thinking, increasingly fails to do so. It does not have to go that way. Another rider, a medical director, uses AI constantly and well, precisely because he has the judgment to catch it when it drifts. That is the tell: the same tool would have sharpened him and dulled the junior lawyer, and the difference was the loop each of them already had. We have been building systems that take the consequence out of the consequential work and leave it in the menial. That is not a flattering thing to discover from behind the wheel, but it is the clearest view I have had of it.
You Cannot See It From Inside
You might think the answer is simply to pay attention, to stay engaged, to refuse to let the loop go slack. I would have thought so too. But my own story is the counterexample, and it is the reason I am not optimistic about vigilance.
I could not see my own open loop. I had every means to fix it: time, purpose, the obvious option to go volunteer, teach, or join something. I did none of it for a year, until economic necessity forced me into the car. The thing that was wrong with my situation was precisely the thing I could not feel from inside it.
The AI user is in the same position, only worse, because the slack loop hides itself. Driving gives you hard ground truth: the wrong turn is simply wrong, and you know it now. Knowledge work rarely offers that, since its consequences are social, institutional, and delayed, which is exactly the cover a slack loop needs. The missing consequence is exactly the signal that would have told you it was missing. The work goes out, nothing pushes back, and you read the absence of pushback as success rather than the warning it is. You cannot introspect your way to noticing a feedback loop that has quietly stopped giving feedback. Self-diagnosis fails at the exact point where you need it.
Architecture, Not Willpower
If the loop will not preserve itself, and we cannot reliably notice when it fails, then it has to be built in. Not encouraged. Built in. This is the whole of the argument I have been making under the name Augmented Human Intelligence, and the driver’s seat is the cleanest illustration of it I have found.
Augmentation, done well, is closer to power steering than to autopilot. The tool makes the turn easier; you still choose the angle, and you still feel the road through your hands. It requires judgment rather than supplying it, leaves the consequence attached to the person who acts, and makes the feedback land where it can still teach. Displacement does the opposite: it removes the human from the loop and calls the removal progress. The difference is not how advanced the tool is. It is how the work is architected around it.
None of this is the default, and it is worth being honest about why. The slack loop is cheaper. It ships faster, it trims headcount, it demos beautifully, and the bill for the judgment we stopped building does not come due this quarter. Left to the market, the slack loop wins. Choosing the other path is a deliberate act, and what that architecture should look like inside a firm, a profession, or a classroom is the harder question, and the one I think the debate should be having.
I did not expect a car to make that case better than a whitepaper. But the economics that put me there built, almost by accident, a job with the loop fully intact: attention that matters, action that counts, consequence I cannot escape, feedback every single time. The systems we are most proud of are quietly removing all four. The task in front of us is to put them back, on purpose, because no one will feel their absence until it is too late to rebuild the judgment that went with them.
Next Tuesday: why the smoothest delivery deserves the most scrutiny, and why I start with mine.
Originally published on Substack.
